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Advanced MongoDB semantic search with research-backed semantic chunking, voyage-context-3 (1024d) embeddings, and RRF hybrid search. Production-ready with monitoring.

Package Exports

  • mongodocs-mcp
  • mongodocs-mcp/dist/index.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (mongodocs-mcp) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

mongodocs-mcp

MongoDB documentation MCP server for Claude/Cursor. Search MongoDB docs, tutorials, and AI examples directly in your AI editor.

What This Does

Provides MongoDB knowledge to Claude/Cursor through semantic search of:

  • Official MongoDB documentation
  • GenAI examples (RAG, Vector Search)
  • Production code examples
  • Interactive tutorials

Prerequisites

  1. MongoDB Atlas account (free tier works)
  2. Voyage AI API key for embeddings
  3. Node.js 18+
  4. Cursor or Claude Desktop

Installation

Step 1: Get Your Keys

MongoDB Atlas Connection String

  1. Go to MongoDB Atlas
  2. Create free cluster (M0)
  3. Click "Connect" → "Drivers"
  4. Copy connection string (starts with mongodb+srv://)

Voyage AI API Key

  1. Go to Voyage AI
  2. Sign up and get API key
  3. Copy key (starts with pa-)

Step 2: Install Package

npm install -g mongodocs-mcp

Step 3: Index Documentation (One-Time Setup)

# Set your keys
export MONGODB_URI="mongodb+srv://YOUR_CONNECTION_STRING"
export VOYAGE_API_KEY="pa-YOUR_API_KEY"

# Index documentation (takes ~15 minutes)
mongodocs-index

This downloads and indexes ~10,000 documents from:

  • MongoDB official docs
  • GenAI Showcase (3.9k stars)
  • MongoDB Chatbot (RAG implementation)
  • Vector Search tutorials
  • Multimodal AI examples

Step 4: Configure Cursor

Add to .cursor/settings.json in your project:

{
  "mcpServers": {
    "mongodocs": {
      "command": "npx",
      "args": ["mongodocs-mcp"],
      "env": {
        "MONGODB_URI": "mongodb+srv://YOUR_CONNECTION_STRING",
        "VOYAGE_API_KEY": "pa-YOUR_API_KEY"
      }
    }
  }
}

Restart Cursor after adding configuration.

Usage

In Cursor/Claude

Ask questions like:

  • "How do I implement vector search in MongoDB?"
  • "Show me RAG examples with MongoDB"
  • "How to create a search index?"

The MCP provides three search methods:

  • Semantic search - finds conceptually related docs
  • Keyword search - exact term matching
  • Hybrid search - combines both methods

Keep Documentation Updated

# Weekly update (only re-indexes changed files)
mongodocs-index update

CLI Commands

mongodocs-index          # Full index (first time)
mongodocs-index update   # Smart update (only changed files)
mongodocs-index clean    # Clear database
mongodocs-index stats    # Show statistics
mongodocs-index test     # Test embeddings

Troubleshooting

"Database is empty"

Run mongodocs-index to populate database.

"Dimension mismatch error"

Clean and re-index:

mongodocs-index clean
mongodocs-index

"Connection refused"

Check MongoDB URI and network access:

  1. Atlas → Network Access → Add your IP
  2. Verify connection string is correct

"Rate limit exceeded"

Voyage AI free tier limit hit. Wait or upgrade plan.

Architecture

  • Embeddings: voyage-context-3 (1024 dimensions)
  • Database: MongoDB Atlas
  • Search: Vector search + text search with RRF
  • Repositories: 5 curated MongoDB repos
  • Updates: Git-based incremental indexing

Performance

  • Initial index: ~15 minutes
  • Smart update: ~2 minutes
  • Search latency: <500ms
  • Documents: ~10,000
  • Storage: ~200MB in MongoDB

Development

# Clone repo
git clone https://github.com/romiluz/mongodocs-mcp.git
cd mongodocs-mcp

# Install dependencies
npm install

# Build
npm run build

# Run locally
npm start

License

MIT

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